Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations99955
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.5 MiB
Average record size in memory184.0 B

Variable types

Categorical12
Numeric11

Alerts

category is highly overall correlated with cost and 6 other fieldsHigh correlation
cost is highly overall correlated with category and 7 other fieldsHigh correlation
current_price is highly overall correlated with profit_margin and 3 other fieldsHigh correlation
discount_pct is highly overall correlated with ratioHigh correlation
gender is highly overall correlated with category and 4 other fieldsHigh correlation
has_extra_sizes is highly overall correlated with category and 4 other fieldsHigh correlation
main_color is highly overall correlated with category and 6 other fieldsHigh correlation
month is highly overall correlated with week_numberHigh correlation
productgroup is highly overall correlated with category and 3 other fieldsHigh correlation
profit_margin is highly overall correlated with cost and 4 other fieldsHigh correlation
promo1 is highly overall correlated with week_numberHigh correlation
ratio is highly overall correlated with discount_pctHigh correlation
regular_price is highly overall correlated with current_price and 3 other fieldsHigh correlation
sales is highly overall correlated with total_profitHigh correlation
sec_color is highly overall correlated with category and 4 other fieldsHigh correlation
style is highly overall correlated with category and 3 other fieldsHigh correlation
total_profit is highly overall correlated with current_price and 4 other fieldsHigh correlation
unit_profit is highly overall correlated with current_price and 3 other fieldsHigh correlation
week_number is highly overall correlated with month and 1 other fieldsHigh correlation
has_extra_sizes is highly imbalanced (53.1%)Imbalance
promo1 is highly imbalanced (66.5%)Imbalance
promo2 is highly imbalanced (95.5%)Imbalance
main_color is uniformly distributedUniform
discount_pct has 1490 (1.5%) zerosZeros

Reproduction

Analysis started2025-05-09 11:31:36.101153
Analysis finished2025-05-09 11:31:56.101776
Duration20 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

country
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
Germany
49380 
Austria
35130 
France
15445 

Length

Max length7
Median length7
Mean length6.8454805
Min length6

Characters and Unicode

Total characters684240
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowGermany
3rd rowGermany
4th rowGermany
5th rowGermany

Common Values

ValueCountFrequency (%)
Germany 49380
49.4%
Austria 35130
35.1%
France 15445
 
15.5%

Length

2025-05-09T14:31:56.249859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:56.362768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
germany 49380
49.4%
austria 35130
35.1%
france 15445
 
15.5%

Most occurring characters

ValueCountFrequency (%)
r 99955
14.6%
a 99955
14.6%
e 64825
9.5%
n 64825
9.5%
G 49380
7.2%
m 49380
7.2%
y 49380
7.2%
A 35130
 
5.1%
u 35130
 
5.1%
s 35130
 
5.1%
Other values (4) 101150
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 684240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 99955
14.6%
a 99955
14.6%
e 64825
9.5%
n 64825
9.5%
G 49380
7.2%
m 49380
7.2%
y 49380
7.2%
A 35130
 
5.1%
u 35130
 
5.1%
s 35130
 
5.1%
Other values (4) 101150
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 684240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 99955
14.6%
a 99955
14.6%
e 64825
9.5%
n 64825
9.5%
G 49380
7.2%
m 49380
7.2%
y 49380
7.2%
A 35130
 
5.1%
u 35130
 
5.1%
s 35130
 
5.1%
Other values (4) 101150
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 684240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 99955
14.6%
a 99955
14.6%
e 64825
9.5%
n 64825
9.5%
G 49380
7.2%
m 49380
7.2%
y 49380
7.2%
A 35130
 
5.1%
u 35130
 
5.1%
s 35130
 
5.1%
Other values (4) 101150
14.8%

productgroup
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
SHOES
59975 
HARDWARE ACCESSORIES
19990 
SHORTS
9996 
SWEATSHIRTS
9994 

Length

Max length20
Median length5
Mean length8.6997649
Min length5

Characters and Unicode

Total characters869585
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHOES
2nd rowSHORTS
3rd rowHARDWARE ACCESSORIES
4th rowSHOES
5th rowSHOES

Common Values

ValueCountFrequency (%)
SHOES 59975
60.0%
HARDWARE ACCESSORIES 19990
 
20.0%
SHORTS 9996
 
10.0%
SWEATSHIRTS 9994
 
10.0%

Length

2025-05-09T14:31:56.494256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:56.602602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
shoes 59975
50.0%
hardware 19990
 
16.7%
accessories 19990
 
16.7%
shorts 9996
 
8.3%
sweatshirts 9994
 
8.3%

Most occurring characters

ValueCountFrequency (%)
S 229894
26.4%
E 129939
14.9%
H 99955
11.5%
O 89961
 
10.3%
R 79960
 
9.2%
A 69964
 
8.0%
C 39980
 
4.6%
W 29984
 
3.4%
I 29984
 
3.4%
T 29984
 
3.4%
Other values (2) 39980
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 869585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 229894
26.4%
E 129939
14.9%
H 99955
11.5%
O 89961
 
10.3%
R 79960
 
9.2%
A 69964
 
8.0%
C 39980
 
4.6%
W 29984
 
3.4%
I 29984
 
3.4%
T 29984
 
3.4%
Other values (2) 39980
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 869585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 229894
26.4%
E 129939
14.9%
H 99955
11.5%
O 89961
 
10.3%
R 79960
 
9.2%
A 69964
 
8.0%
C 39980
 
4.6%
W 29984
 
3.4%
I 29984
 
3.4%
T 29984
 
3.4%
Other values (2) 39980
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 869585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 229894
26.4%
E 129939
14.9%
H 99955
11.5%
O 89961
 
10.3%
R 79960
 
9.2%
A 69964
 
8.0%
C 39980
 
4.6%
W 29984
 
3.4%
I 29984
 
3.4%
T 29984
 
3.4%
Other values (2) 39980
 
4.6%

category
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
TRAINING
29986 
RUNNING
19992 
FOOTBALL GENERIC
19991 
RELAX CASUAL
9998 
GOLF
9994 

Length

Max length16
Median length12
Mean length9.2001801
Min length4

Characters and Unicode

Total characters919604
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAINING
2nd rowTRAINING
3rd rowGOLF
4th rowRUNNING
5th rowRELAX CASUAL

Common Values

ValueCountFrequency (%)
TRAINING 29986
30.0%
RUNNING 19992
20.0%
FOOTBALL GENERIC 19991
20.0%
RELAX CASUAL 9998
 
10.0%
GOLF 9994
 
10.0%
INDOOR 9994
 
10.0%

Length

2025-05-09T14:31:56.727255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:56.837917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
training 29986
23.1%
running 19992
15.4%
football 19991
15.4%
generic 19991
15.4%
relax 9998
 
7.7%
casual 9998
 
7.7%
golf 9994
 
7.7%
indoor 9994
 
7.7%

Most occurring characters

ValueCountFrequency (%)
N 149933
16.3%
I 109949
12.0%
R 89961
9.8%
A 79971
8.7%
G 79963
8.7%
L 69972
7.6%
O 69964
7.6%
E 49980
 
5.4%
T 49977
 
5.4%
U 29990
 
3.3%
Other values (7) 139944
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 919604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 149933
16.3%
I 109949
12.0%
R 89961
9.8%
A 79971
8.7%
G 79963
8.7%
L 69972
7.6%
O 69964
7.6%
E 49980
 
5.4%
T 49977
 
5.4%
U 29990
 
3.3%
Other values (7) 139944
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 919604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 149933
16.3%
I 109949
12.0%
R 89961
9.8%
A 79971
8.7%
G 79963
8.7%
L 69972
7.6%
O 69964
7.6%
E 49980
 
5.4%
T 49977
 
5.4%
U 29990
 
3.3%
Other values (7) 139944
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 919604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 149933
16.3%
I 109949
12.0%
R 89961
9.8%
A 79971
8.7%
G 79963
8.7%
L 69972
7.6%
O 69964
7.6%
E 49980
 
5.4%
T 49977
 
5.4%
U 29990
 
3.3%
Other values (7) 139944
15.2%

style
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
regular
49979 
wide
29984 
slim
19992 

Length

Max length7
Median length7
Mean length5.500045
Min length4

Characters and Unicode

Total characters549757
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowslim
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular 49979
50.0%
wide 29984
30.0%
slim 19992
20.0%

Length

2025-05-09T14:31:56.972316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:57.070476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
regular 49979
50.0%
wide 29984
30.0%
slim 19992
20.0%

Most occurring characters

ValueCountFrequency (%)
r 99958
18.2%
e 79963
14.5%
l 69971
12.7%
g 49979
9.1%
u 49979
9.1%
a 49979
9.1%
i 49976
9.1%
w 29984
 
5.5%
d 29984
 
5.5%
s 19992
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 549757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 99958
18.2%
e 79963
14.5%
l 69971
12.7%
g 49979
9.1%
u 49979
9.1%
a 49979
9.1%
i 49976
9.1%
w 29984
 
5.5%
d 29984
 
5.5%
s 19992
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 549757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 99958
18.2%
e 79963
14.5%
l 69971
12.7%
g 49979
9.1%
u 49979
9.1%
a 49979
9.1%
i 49976
9.1%
w 29984
 
5.5%
d 29984
 
5.5%
s 19992
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 549757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 99958
18.2%
e 79963
14.5%
l 69971
12.7%
g 49979
9.1%
u 49979
9.1%
a 49979
9.1%
i 49976
9.1%
w 29984
 
5.5%
d 29984
 
5.5%
s 19992
 
3.6%

gender
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
women
69968 
kids
9996 
unisex
9996 
men
9995 

Length

Max length6
Median length5
Mean length4.80001
Min length3

Characters and Unicode

Total characters479785
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwomen
2nd rowwomen
3rd rowwomen
4th rowkids
5th rowwomen

Common Values

ValueCountFrequency (%)
women 69968
70.0%
kids 9996
 
10.0%
unisex 9996
 
10.0%
men 9995
 
10.0%

Length

2025-05-09T14:31:57.189034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:57.298215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
women 69968
70.0%
kids 9996
 
10.0%
unisex 9996
 
10.0%
men 9995
 
10.0%

Most occurring characters

ValueCountFrequency (%)
e 89959
18.7%
n 89959
18.7%
m 79963
16.7%
w 69968
14.6%
o 69968
14.6%
i 19992
 
4.2%
s 19992
 
4.2%
k 9996
 
2.1%
d 9996
 
2.1%
u 9996
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 479785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 89959
18.7%
n 89959
18.7%
m 79963
16.7%
w 69968
14.6%
o 69968
14.6%
i 19992
 
4.2%
s 19992
 
4.2%
k 9996
 
2.1%
d 9996
 
2.1%
u 9996
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 479785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 89959
18.7%
n 89959
18.7%
m 79963
16.7%
w 69968
14.6%
o 69968
14.6%
i 19992
 
4.2%
s 19992
 
4.2%
k 9996
 
2.1%
d 9996
 
2.1%
u 9996
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 479785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 89959
18.7%
n 89959
18.7%
m 79963
16.7%
w 69968
14.6%
o 69968
14.6%
i 19992
 
4.2%
s 19992
 
4.2%
k 9996
 
2.1%
d 9996
 
2.1%
u 9996
 
2.1%

main_color
Categorical

HIGH CORRELATION  UNIFORM 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
blueviolet
9998 
chocolate
9996 
darkkhaki
9996 
rosybrown
9996 
brown
9996 
Other values (5)
49973 

Length

Max length12
Median length10
Mean length8.19997
Min length4

Characters and Unicode

Total characters819628
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowchocolate
2nd rowdarkkhaki
3rd rowgoldenrod
4th rowrosybrown
5th rowblueviolet

Common Values

ValueCountFrequency (%)
blueviolet 9998
10.0%
chocolate 9996
10.0%
darkkhaki 9996
10.0%
rosybrown 9996
10.0%
brown 9996
10.0%
silver 9996
10.0%
gray 9995
10.0%
goldenrod 9994
10.0%
steelblue 9994
10.0%
lightskyblue 9994
10.0%

Length

2025-05-09T14:31:57.449244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:57.611486image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
blueviolet 9998
10.0%
chocolate 9996
10.0%
darkkhaki 9996
10.0%
rosybrown 9996
10.0%
brown 9996
10.0%
silver 9996
10.0%
gray 9995
10.0%
goldenrod 9994
10.0%
steelblue 9994
10.0%
lightskyblue 9994
10.0%

Most occurring characters

ValueCountFrequency (%)
e 89958
 
11.0%
l 89958
 
11.0%
o 79966
 
9.8%
r 69969
 
8.5%
b 49978
 
6.1%
i 39984
 
4.9%
a 39983
 
4.9%
t 39982
 
4.9%
k 39982
 
4.9%
s 39980
 
4.9%
Other values (9) 239888
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 819628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 89958
 
11.0%
l 89958
 
11.0%
o 79966
 
9.8%
r 69969
 
8.5%
b 49978
 
6.1%
i 39984
 
4.9%
a 39983
 
4.9%
t 39982
 
4.9%
k 39982
 
4.9%
s 39980
 
4.9%
Other values (9) 239888
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 819628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 89958
 
11.0%
l 89958
 
11.0%
o 79966
 
9.8%
r 69969
 
8.5%
b 49978
 
6.1%
i 39984
 
4.9%
a 39983
 
4.9%
t 39982
 
4.9%
k 39982
 
4.9%
s 39980
 
4.9%
Other values (9) 239888
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 819628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 89958
 
11.0%
l 89958
 
11.0%
o 79966
 
9.8%
r 69969
 
8.5%
b 49978
 
6.1%
i 39984
 
4.9%
a 39983
 
4.9%
t 39982
 
4.9%
k 39982
 
4.9%
s 39980
 
4.9%
Other values (9) 239888
29.3%

sec_color
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
rosybrown
39981 
lightblue
29988 
lavender
29986 

Length

Max length9
Median length9
Mean length8.700005
Min length8

Characters and Unicode

Total characters869609
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlavender
2nd rowlavender
3rd rowlavender
4th rowlightblue
5th rowlightblue

Common Values

ValueCountFrequency (%)
rosybrown 39981
40.0%
lightblue 29988
30.0%
lavender 29986
30.0%

Length

2025-05-09T14:31:57.782231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:57.881186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rosybrown 39981
40.0%
lightblue 29988
30.0%
lavender 29986
30.0%

Most occurring characters

ValueCountFrequency (%)
r 109948
12.6%
l 89962
10.3%
e 89960
10.3%
o 79962
 
9.2%
b 69969
 
8.0%
n 69967
 
8.0%
s 39981
 
4.6%
y 39981
 
4.6%
w 39981
 
4.6%
t 29988
 
3.4%
Other values (7) 209910
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 869609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 109948
12.6%
l 89962
10.3%
e 89960
10.3%
o 79962
 
9.2%
b 69969
 
8.0%
n 69967
 
8.0%
s 39981
 
4.6%
y 39981
 
4.6%
w 39981
 
4.6%
t 29988
 
3.4%
Other values (7) 209910
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 869609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 109948
12.6%
l 89962
10.3%
e 89960
10.3%
o 79962
 
9.2%
b 69969
 
8.0%
n 69967
 
8.0%
s 39981
 
4.6%
y 39981
 
4.6%
w 39981
 
4.6%
t 29988
 
3.4%
Other values (7) 209910
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 869609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 109948
12.6%
l 89962
10.3%
e 89960
10.3%
o 79962
 
9.2%
b 69969
 
8.0%
n 69967
 
8.0%
s 39981
 
4.6%
y 39981
 
4.6%
w 39981
 
4.6%
t 29988
 
3.4%
Other values (7) 209910
24.1%

has_extra_sizes
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
1
89959 
0
9996 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 89959
90.0%
0 9996
 
10.0%

Length

2025-05-09T14:31:57.987238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:58.078135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 89959
90.0%
0 9996
 
10.0%

Most occurring characters

ValueCountFrequency (%)
1 89959
90.0%
0 9996
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 89959
90.0%
0 9996
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 89959
90.0%
0 9996
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 89959
90.0%
0 9996
 
10.0%

year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
2015
42790 
2016
41792 
2017
14593 
2014
 
780

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters399820
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2015 42790
42.8%
2016 41792
41.8%
2017 14593
 
14.6%
2014 780
 
0.8%

Length

2025-05-09T14:31:58.175760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:31:58.270202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2015 42790
42.8%
2016 41792
41.8%
2017 14593
 
14.6%
2014 780
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2 99955
25.0%
0 99955
25.0%
1 99955
25.0%
5 42790
10.7%
6 41792
10.5%
7 14593
 
3.6%
4 780
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 399820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 99955
25.0%
0 99955
25.0%
1 99955
25.0%
5 42790
10.7%
6 41792
10.5%
7 14593
 
3.6%
4 780
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 399820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 99955
25.0%
0 99955
25.0%
1 99955
25.0%
5 42790
10.7%
6 41792
10.5%
7 14593
 
3.6%
4 780
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 399820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 99955
25.0%
0 99955
25.0%
1 99955
25.0%
5 42790
10.7%
6 41792
10.5%
7 14593
 
3.6%
4 780
 
0.2%

month
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9063379
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.0 KiB
2025-05-09T14:31:58.376432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5316156
Coefficient of variation (CV)0.5979366
Kurtosis-1.2261821
Mean5.9063379
Median Absolute Deviation (MAD)3
Skewness0.23773583
Sum590368
Variance12.472309
MonotonicityNot monotonic
2025-05-09T14:31:58.488330image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 11730
11.7%
4 10670
10.7%
3 10183
10.2%
2 9967
10.0%
5 8540
8.5%
11 7550
7.6%
10 7100
7.1%
12 7080
7.1%
8 7080
7.1%
7 6995
7.0%
Other values (2) 13060
13.1%
ValueCountFrequency (%)
1 11730
11.7%
2 9967
10.0%
3 10183
10.2%
4 10670
10.7%
5 8540
8.5%
6 6460
6.5%
7 6995
7.0%
8 7080
7.1%
9 6600
6.6%
10 7100
7.1%
ValueCountFrequency (%)
12 7080
7.1%
11 7550
7.6%
10 7100
7.1%
9 6600
6.6%
8 7080
7.1%
7 6995
7.0%
6 6460
6.5%
5 8540
8.5%
4 10670
10.7%
3 10183
10.2%

week_number
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.347366
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.0 KiB
2025-05-09T14:31:58.619888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q338
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)27

Descriptive statistics

Standard deviation15.616134
Coefficient of variation (CV)0.64138904
Kurtosis-1.2280118
Mean24.347366
Median Absolute Deviation (MAD)13
Skewness0.23281323
Sum2433641
Variance243.86364
MonotonicityNot monotonic
2025-05-09T14:31:58.759867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 2770
 
2.8%
5 2694
 
2.7%
4 2650
 
2.7%
15 2630
 
2.6%
17 2620
 
2.6%
9 2573
 
2.6%
16 2510
 
2.5%
2 2450
 
2.5%
6 2450
 
2.5%
7 2420
 
2.4%
Other values (43) 74188
74.2%
ValueCountFrequency (%)
1 2260
2.3%
2 2450
2.5%
3 2770
2.8%
4 2650
2.7%
5 2694
2.7%
6 2450
2.5%
7 2420
2.4%
8 2403
2.4%
9 2573
2.6%
10 2310
2.3%
ValueCountFrequency (%)
53 740
 
0.7%
52 2360
2.4%
51 1790
1.8%
50 1520
1.5%
49 1490
1.5%
48 1690
1.7%
47 1960
2.0%
46 1440
1.4%
45 1720
1.7%
44 1520
1.5%

regular_price
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.406195
Minimum3.95
Maximum197.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.0 KiB
2025-05-09T14:31:58.894706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.95
5-th percentile6.95
Q125.95
median40.95
Q379.95
95-th percentile120.95
Maximum197.95
Range194
Interquartile range (IQR)54

Descriptive statistics

Standard deviation35.272212
Coefficient of variation (CV)0.67305423
Kurtosis0.32165788
Mean52.406195
Median Absolute Deviation (MAD)20
Skewness0.90334597
Sum5238261.2
Variance1244.1289
MonotonicityNot monotonic
2025-05-09T14:31:59.055527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.95 3620
 
3.6%
29.95 3155
 
3.2%
30.95 3120
 
3.1%
23.95 3103
 
3.1%
62.95 2690
 
2.7%
25.95 2540
 
2.5%
44.95 2420
 
2.4%
20.95 2330
 
2.3%
3.95 2080
 
2.1%
83.95 1920
 
1.9%
Other values (113) 72977
73.0%
ValueCountFrequency (%)
3.95 2080
2.1%
4.95 570
 
0.6%
5.95 1270
1.3%
6.95 1323
1.3%
7.95 170
 
0.2%
8.95 680
 
0.7%
9.95 510
 
0.5%
10.95 800
 
0.8%
11.95 130
 
0.1%
12.95 190
 
0.2%
ValueCountFrequency (%)
197.95 120
 
0.1%
195.95 160
 
0.2%
153.95 850
0.9%
150.95 150
 
0.2%
141.95 90
 
0.1%
139.95 240
 
0.2%
136.95 200
 
0.2%
135.95 270
 
0.3%
134.95 150
 
0.2%
132.95 490
0.5%

current_price
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.282169
Minimum1.95
Maximum139.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.0 KiB
2025-05-09T14:31:59.209303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.95
5-th percentile3.95
Q111.95
median20.95
Q337.95
95-th percentile74.95
Maximum139.45
Range137.5
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.488396
Coefficient of variation (CV)0.79514394
Kurtosis2.4630586
Mean28.282169
Median Absolute Deviation (MAD)11
Skewness1.4974943
Sum2826944.2
Variance505.72794
MonotonicityNot monotonic
2025-05-09T14:31:59.358051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.95 3650
 
3.7%
9.95 3360
 
3.4%
11.95 3230
 
3.2%
13.95 3130
 
3.1%
17.95 2930
 
2.9%
12.95 2920
 
2.9%
16.95 2883
 
2.9%
15.95 2720
 
2.7%
7.95 2670
 
2.7%
14.95 2520
 
2.5%
Other values (124) 69942
70.0%
ValueCountFrequency (%)
1.95 1720
1.7%
2.95 1990
2.0%
3.95 1420
 
1.4%
4.95 1650
1.7%
5.95 1953
2.0%
6.95 2160
2.2%
7.95 2670
2.7%
8.95 3650
3.7%
9.95 3360
3.4%
10.95 2400
2.4%
ValueCountFrequency (%)
139.45 100
0.1%
136.95 10
 
< 0.1%
135.95 10
 
< 0.1%
134.95 10
 
< 0.1%
133.95 20
 
< 0.1%
131.95 20
 
< 0.1%
129.95 10
 
< 0.1%
128.95 20
 
< 0.1%
127.95 10
 
< 0.1%
125.95 30
 
< 0.1%

ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct2722
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54561339
Minimum0.29648241
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.0 KiB
2025-05-09T14:31:59.510749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.29648241
5-th percentile0.30283224
Q10.35483871
median0.5250212
Q30.69924812
95-th percentile0.88868275
Maximum1
Range0.70351759
Interquartile range (IQR)0.34440941

Descriptive statistics

Standard deviation0.19435305
Coefficient of variation (CV)0.35621019
Kurtosis-0.91070921
Mean0.54561339
Median Absolute Deviation (MAD)0.17122476
Skewness0.39812175
Sum54536.786
Variance0.037773107
MonotonicityNot monotonic
2025-05-09T14:31:59.850304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1490
 
1.5%
0.4936708861 1130
 
1.1%
0.3103448276 830
 
0.8%
0.332096475 820
 
0.8%
0.3214862682 760
 
0.8%
0.2988313856 730
 
0.7%
0.746835443 720
 
0.7%
0.3319415449 690
 
0.7%
0.3317422434 570
 
0.6%
0.3548387097 510
 
0.5%
Other values (2712) 91705
91.7%
ValueCountFrequency (%)
0.2964824121 120
 
0.1%
0.298245614 230
 
0.2%
0.2988313856 730
0.7%
0.2991239049 140
 
0.1%
0.2992992993 160
 
0.2%
0.2994161802 40
 
< 0.1%
0.2994996426 310
0.3%
0.2995622264 50
 
0.1%
0.2996108949 60
 
0.1%
0.2996498249 70
 
0.1%
ValueCountFrequency (%)
1 1490
1.5%
0.9920603414 10
 
< 0.1%
0.9917321207 10
 
< 0.1%
0.9915218313 10
 
< 0.1%
0.9904716532 10
 
< 0.1%
0.9899949975 10
 
< 0.1%
0.9890049478 10
 
< 0.1%
0.9880881477 10
 
< 0.1%
0.9857040743 20
 
< 0.1%
0.9841206828 10
 
< 0.1%

discount_pct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2722
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45438661
Minimum0
Maximum0.70351759
Zeros1490
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size781.0 KiB
2025-05-09T14:32:00.004178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11131725
Q10.30075188
median0.4749788
Q30.64516129
95-th percentile0.69716776
Maximum0.70351759
Range0.70351759
Interquartile range (IQR)0.34440941

Descriptive statistics

Standard deviation0.19435305
Coefficient of variation (CV)0.42772617
Kurtosis-0.91070921
Mean0.45438661
Median Absolute Deviation (MAD)0.17122476
Skewness-0.39812175
Sum45418.214
Variance0.037773107
MonotonicityNot monotonic
2025-05-09T14:32:00.151596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1490
 
1.5%
0.5063291139 1130
 
1.1%
0.6896551724 830
 
0.8%
0.667903525 820
 
0.8%
0.6785137318 760
 
0.8%
0.7011686144 730
 
0.7%
0.253164557 720
 
0.7%
0.6680584551 690
 
0.7%
0.6682577566 570
 
0.6%
0.6451612903 510
 
0.5%
Other values (2712) 91705
91.7%
ValueCountFrequency (%)
0 1490
1.5%
0.007939658595 10
 
< 0.1%
0.008267879289 10
 
< 0.1%
0.008478168716 10
 
< 0.1%
0.009528346832 10
 
< 0.1%
0.0100050025 10
 
< 0.1%
0.01099505223 10
 
< 0.1%
0.01191185229 10
 
< 0.1%
0.01429592566 20
 
< 0.1%
0.01587931719 10
 
< 0.1%
ValueCountFrequency (%)
0.7035175879 120
 
0.1%
0.701754386 230
 
0.2%
0.7011686144 730
0.7%
0.7008760951 140
 
0.1%
0.7007007007 160
 
0.2%
0.7005838198 40
 
< 0.1%
0.7005003574 310
0.3%
0.7004377736 50
 
0.1%
0.7003891051 60
 
0.1%
0.7003501751 70
 
0.1%

cost
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5172089
Minimum1.29
Maximum13.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.0 KiB
2025-05-09T14:32:00.277955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.29
5-th percentile1.29
Q12.29
median8.7
Q39.6
95-th percentile13.29
Maximum13.29
Range12
Interquartile range (IQR)7.31

Descriptive statistics

Standard deviation3.9147665
Coefficient of variation (CV)0.60068145
Kurtosis-1.2873369
Mean6.5172089
Median Absolute Deviation (MAD)3.5
Skewness0.099245499
Sum651427.62
Variance15.325397
MonotonicityNot monotonic
2025-05-09T14:32:00.388527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9.6 9998
10.0%
13.29 9996
10.0%
2.29 9996
10.0%
9 9996
10.0%
9.9 9996
10.0%
1.29 9996
10.0%
8.7 9995
10.0%
1.7 9994
10.0%
4.2 9994
10.0%
5.2 9994
10.0%
ValueCountFrequency (%)
1.29 9996
10.0%
1.7 9994
10.0%
2.29 9996
10.0%
4.2 9994
10.0%
5.2 9994
10.0%
8.7 9995
10.0%
9 9996
10.0%
9.6 9998
10.0%
9.9 9996
10.0%
13.29 9996
10.0%
ValueCountFrequency (%)
13.29 9996
10.0%
9.9 9996
10.0%
9.6 9998
10.0%
9 9996
10.0%
8.7 9995
10.0%
5.2 9994
10.0%
4.2 9994
10.0%
2.29 9996
10.0%
1.7 9994
10.0%
1.29 9996
10.0%

sales
Real number (ℝ)

HIGH CORRELATION 

Distinct327
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.721455
Minimum1
Maximum344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.0 KiB
2025-05-09T14:32:00.524427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median26
Q364
95-th percentile216
Maximum344
Range343
Interquartile range (IQR)54

Descriptive statistics

Standard deviation71.398308
Coefficient of variation (CV)1.3290464
Kurtosis5.7159808
Mean53.721455
Median Absolute Deviation (MAD)20
Skewness2.3656483
Sum5369728
Variance5097.7184
MonotonicityNot monotonic
2025-05-09T14:32:00.673253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3073
 
3.1%
1 3053
 
3.1%
3 2950
 
3.0%
4 2800
 
2.8%
5 2680
 
2.7%
6 2670
 
2.7%
7 2380
 
2.4%
8 2370
 
2.4%
9 2160
 
2.2%
11 2130
 
2.1%
Other values (317) 73689
73.7%
ValueCountFrequency (%)
1 3053
3.1%
2 3073
3.1%
3 2950
3.0%
4 2800
2.8%
5 2680
2.7%
6 2670
2.7%
7 2380
2.4%
8 2370
2.4%
9 2160
2.2%
10 1940
1.9%
ValueCountFrequency (%)
344 1960
2.0%
343 20
 
< 0.1%
342 40
 
< 0.1%
339 10
 
< 0.1%
337 30
 
< 0.1%
333 50
 
0.1%
332 10
 
< 0.1%
331 10
 
< 0.1%
328 10
 
< 0.1%
327 20
 
< 0.1%

unit_profit
Real number (ℝ)

HIGH CORRELATION 

Distinct866
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.767545
Minimum-11.34
Maximum136.135
Zeros0
Zeros (%)0.0%
Negative10796
Negative (%)10.8%
Memory size781.0 KiB
2025-05-09T14:32:00.808263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-11.34
5-th percentile-3.34
Q15.66
median15.35
Q331.75
95-th percentile68.75
Maximum136.135
Range147.475
Interquartile range (IQR)26.09

Descriptive statistics

Standard deviation22.839104
Coefficient of variation (CV)1.0492274
Kurtosis2.3575666
Mean21.767545
Median Absolute Deviation (MAD)11.69
Skewness1.4366252
Sum2175775
Variance521.62468
MonotonicityNot monotonic
2025-05-09T14:32:00.941535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.66 796
 
0.8%
11.66 785
 
0.8%
8.66 766
 
0.8%
12.66 764
 
0.8%
9.66 753
 
0.8%
15.66 738
 
0.7%
14.66 729
 
0.7%
13.66 723
 
0.7%
4.66 705
 
0.7%
4.75 701
 
0.7%
Other values (856) 92495
92.5%
ValueCountFrequency (%)
-11.34 172
0.2%
-10.34 199
0.2%
-9.34 142
0.1%
-8.34 165
0.2%
-7.95 172
0.2%
-7.65 172
0.2%
-7.34 195
0.2%
-7.05 172
0.2%
-6.95 199
0.2%
-6.75 172
0.2%
ValueCountFrequency (%)
136.135 81
0.1%
136.05 2
 
< 0.1%
135.95 1
 
< 0.1%
135.75 1
 
< 0.1%
135.66 1
 
< 0.1%
135.35 1
 
< 0.1%
135.25 1
 
< 0.1%
135.05 1
 
< 0.1%
134.66 2
 
< 0.1%
134.25 1
 
< 0.1%

total_profit
Real number (ℝ)

HIGH CORRELATION 

Distinct33211
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean865.32133
Minimum-3842
Maximum6390
Zeros0
Zeros (%)0.0%
Negative10796
Negative (%)10.8%
Memory size781.0 KiB
2025-05-09T14:32:01.081405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3842
5-th percentile-95.42
Q173.4
median316.75
Q3998.48
95-th percentile4348.113
Maximum6390
Range10232
Interquartile range (IQR)925.08

Descriptive statistics

Standard deviation1441.4727
Coefficient of variation (CV)1.6658236
Kurtosis5.6914432
Mean865.32133
Median Absolute Deviation (MAD)298.09
Skewness2.3612155
Sum86493194
Variance2077843.5
MonotonicityNot monotonic
2025-05-09T14:32:01.227972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6390 2704
 
2.7%
123.75 66
 
0.1%
213.75 60
 
0.1%
56.25 60
 
0.1%
78.75 60
 
0.1%
146.25 56
 
0.1%
173.25 56
 
0.1%
94.5 54
 
0.1%
57.75 52
 
0.1%
63 50
 
0.1%
Other values (33201) 96737
96.8%
ValueCountFrequency (%)
-3842 32
< 0.1%
-3776.22 1
 
< 0.1%
-3677.94 1
 
< 0.1%
-3650.02 1
 
< 0.1%
-3627 1
 
< 0.1%
-3611.22 1
 
< 0.1%
-3553.2 1
 
< 0.1%
-3515.05 1
 
< 0.1%
-3457.14 1
 
< 0.1%
-3402 1
 
< 0.1%
ValueCountFrequency (%)
6390 2704
2.7%
6389.34 2
 
< 0.1%
6388.25 1
 
< 0.1%
6388.2 1
 
< 0.1%
6385.54 2
 
< 0.1%
6383.94 1
 
< 0.1%
6382.5 1
 
< 0.1%
6381.72 1
 
< 0.1%
6380 1
 
< 0.1%
6378.75 2
 
< 0.1%

profit_margin
Real number (ℝ)

HIGH CORRELATION 

Distinct1398
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55960661
Minimum-1.5484368
Maximum0.99341669
Zeros0
Zeros (%)0.0%
Negative10796
Negative (%)10.8%
Memory size781.0 KiB
2025-05-09T14:32:01.363847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1.5484368
5-th percentile-0.61344538
Q10.44846797
median0.74965229
Q30.88932806
95-th percentile0.96527068
Maximum0.99341669
Range2.5418535
Interquartile range (IQR)0.4408601

Descriptive statistics

Standard deviation0.52514881
Coefficient of variation (CV)0.93842496
Kurtosis5.8130724
Mean0.55960661
Median Absolute Deviation (MAD)0.1726778
Skewness-2.3473688
Sum55935.479
Variance0.27578128
MonotonicityNot monotonic
2025-05-09T14:32:01.512713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.548436808 2334
 
2.3%
-0.4849162011 365
 
0.4%
0.8100558659 365
 
0.4%
-0.005586592179 365
 
0.4%
-0.07262569832 365
 
0.4%
0.530726257 365
 
0.4%
-0.1061452514 365
 
0.4%
0.4189944134 365
 
0.4%
0.8558659218 365
 
0.4%
0.02793296089 365
 
0.4%
Other values (1388) 94336
94.4%
ValueCountFrequency (%)
-1.548436808 2334
2.3%
-1.506329114 142
 
0.1%
-1.430379747 142
 
0.1%
-1.278481013 142
 
0.1%
-1.233613445 195
 
0.2%
-1.202531646 142
 
0.1%
-1.153846154 172
 
0.2%
-1 165
 
0.2%
-0.9393939394 165
 
0.2%
-0.9122302158 216
 
0.2%
ValueCountFrequency (%)
0.9934166879 1
< 0.1%
0.9927912825 1
< 0.1%
0.9916747338 1
< 0.1%
0.9915658712 2
< 0.1%
0.9913243174 1
< 0.1%
0.9911613566 2
< 0.1%
0.9911003794 1
< 0.1%
0.9909122931 1
< 0.1%
0.9908478184 1
< 0.1%
0.9905805038 1
< 0.1%

promo1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
0
93770 
1
 
6185

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 93770
93.8%
1 6185
 
6.2%

Length

2025-05-09T14:32:01.637858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:32:01.725371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 93770
93.8%
1 6185
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 93770
93.8%
1 6185
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 93770
93.8%
1 6185
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 93770
93.8%
1 6185
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 93770
93.8%
1 6185
 
6.2%

promo2
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
0
99465 
1
 
490

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 99465
99.5%
1 490
 
0.5%

Length

2025-05-09T14:32:01.817803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:32:01.904193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 99465
99.5%
1 490
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 99465
99.5%
1 490
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 99465
99.5%
1 490
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 99465
99.5%
1 490
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 99465
99.5%
1 490
 
0.5%

label
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
0
86031 
1
13924 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99955
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86031
86.1%
1 13924
 
13.9%

Length

2025-05-09T14:32:01.997787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-09T14:32:02.089724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 86031
86.1%
1 13924
 
13.9%

Most occurring characters

ValueCountFrequency (%)
0 86031
86.1%
1 13924
 
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 86031
86.1%
1 13924
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 86031
86.1%
1 13924
 
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 86031
86.1%
1 13924
 
13.9%

Interactions

2025-05-09T14:31:53.871069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:41.300371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.502919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.650350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:44.997568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:46.305084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.594712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.893153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.220991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.506964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.715921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.976241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:41.419769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.605364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.755585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:45.099052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:46.419900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.719826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.998366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.335744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.611788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.814897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:54.076495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:41.519365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.702647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.858212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:45.201750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:46.553758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.842560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:49.097689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.452188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.720934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.912447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:54.194998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:41.632394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.820589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.975785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:45.316908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:46.685086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.977262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:49.209087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.570932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.842182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.027391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:54.306645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:41.736194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.930183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:44.094589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:45.425461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:46.796837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.095699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:49.318349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.684481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.960293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.139307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:54.414280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:41.842240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.035011image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:44.208043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:45.558844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:46.911605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.212080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:49.424430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.802383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.071741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.245638image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:54.713527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:41.950166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.142465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:44.320108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:45.693944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.028926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.336130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:49.696400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.913274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.184928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.362608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:54.827521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.044474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.239149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:44.561305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:45.806603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.138094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.437239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:49.798610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.019507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.284062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.456311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:54.933294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.148127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.344151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:44.669178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:45.941370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.248461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.560496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:49.904060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.129301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.395241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.561734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:55.041123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.265404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.446718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:44.786304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:46.058600image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.363225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.680790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.011300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.242163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.501122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.666503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:55.141306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:42.389040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:43.547737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:44.889966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:46.179515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:47.476400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:48.787178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:50.112238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:51.347965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:52.612628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-05-09T14:31:53.772222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-05-09T14:32:02.180220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
categorycostcountrycurrent_pricediscount_pctgenderhas_extra_sizeslabelmain_colormonthproductgroupprofit_marginpromo1promo2ratioregular_pricesalessec_colorstyletotal_profitunit_profitweek_numberyear
category1.0000.6990.0000.0000.0000.6900.6670.0051.0000.0000.6380.1820.0000.0000.0000.0000.0000.7950.5890.0520.0780.0000.000
cost0.6991.0000.000-0.000-0.0000.7241.0000.0001.000-0.0000.816-0.6580.0000.0000.000-0.000-0.0000.7820.876-0.226-0.225-0.0000.000
country0.0000.0001.0000.0760.0440.0000.0000.0100.0000.0320.0000.0270.0080.1640.0440.1750.0250.0000.0000.0250.0780.0280.014
current_price0.000-0.0000.0761.000-0.3720.0000.0000.1930.000-0.1400.0000.7060.0740.0320.3720.885-0.1780.0000.0000.5910.966-0.1270.034
discount_pct0.000-0.0000.044-0.3721.0000.0000.0000.4620.0000.3390.000-0.2560.1620.044-1.0000.0710.4350.0000.0000.042-0.3630.3120.065
gender0.6900.7240.0000.0000.0001.0001.0000.0001.0000.0000.3090.1920.0000.0000.0000.0000.0000.5460.4830.0500.0800.0000.000
has_extra_sizes0.6671.0000.0000.0000.0001.0001.0000.0001.0000.0000.2720.1920.0000.0000.0000.0000.0000.4080.5090.0540.0870.0000.000
label0.0050.0000.0100.1930.4620.0000.0001.0000.0030.3350.0000.1080.0640.0200.4620.0220.1530.0000.0000.0500.1790.3370.039
main_color1.0001.0000.0000.0000.0001.0001.0000.0031.0000.0001.0000.2090.0000.0000.0000.0000.0001.0001.0000.0680.0950.0000.000
month0.000-0.0000.032-0.1400.3390.0000.0000.3350.0001.0000.000-0.0990.3900.091-0.3390.0060.2000.0000.0000.037-0.1370.9110.309
productgroup0.6380.8160.0000.0000.0000.3090.2720.0001.0000.0001.0000.3260.0000.0000.0000.0000.0000.5530.4940.0980.1440.0000.000
profit_margin0.182-0.6580.0270.706-0.2560.1920.1920.1080.209-0.0990.3261.0000.0490.0110.2560.629-0.1230.1520.0890.5860.844-0.0890.016
promo10.0000.0000.0080.0740.1620.0000.0000.0640.0000.3900.0000.0491.0000.0470.1620.0160.1250.0000.0000.1490.0670.5210.358
promo20.0000.0000.1640.0320.0440.0000.0000.0200.0000.0910.0000.0110.0471.0000.0440.0280.0260.0000.0000.0210.0200.0880.047
ratio0.0000.0000.0440.372-1.0000.0000.0000.4620.000-0.3390.0000.2560.1620.0441.000-0.071-0.4350.0000.000-0.0420.363-0.3120.065
regular_price0.000-0.0000.1750.8850.0710.0000.0000.0220.0000.0060.0000.6290.0160.028-0.0711.0000.0110.0000.0000.6520.8540.0060.019
sales0.000-0.0000.025-0.1780.4350.0000.0000.1530.0000.2000.000-0.1230.1250.026-0.4350.0111.0000.0000.0000.542-0.1740.2010.040
sec_color0.7950.7820.0000.0000.0000.5460.4080.0001.0000.0000.5530.1520.0000.0000.0000.0000.0001.0000.4000.0330.0380.0000.000
style0.5890.8760.0000.0000.0000.4830.5090.0001.0000.0000.4940.0890.0000.0000.0000.0000.0000.4001.0000.0360.0240.0000.000
total_profit0.052-0.2260.0250.5910.0420.0500.0540.0500.0680.0370.0980.5860.1490.021-0.0420.6520.5420.0330.0361.0000.6330.0500.044
unit_profit0.078-0.2250.0780.966-0.3630.0800.0870.1790.095-0.1370.1440.8440.0670.0200.3630.854-0.1740.0380.0240.6331.000-0.1240.028
week_number0.000-0.0000.028-0.1270.3120.0000.0000.3370.0000.9110.000-0.0890.5210.088-0.3120.0060.2010.0000.0000.050-0.1241.0000.292
year0.0000.0000.0140.0340.0650.0000.0000.0390.0000.3090.0000.0160.3580.0470.0650.0190.0400.0000.0000.0440.0280.2921.000

Missing values

2025-05-09T14:31:55.310323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-09T14:31:55.704740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

countryproductgroupcategorystylegendermain_colorsec_colorhas_extra_sizesyearmonthweek_numberregular_pricecurrent_priceratiodiscount_pctcostsalesunit_profittotal_profitprofit_marginpromo1promo2label
0GermanySHOESTRAININGslimwomenchocolatelavender120163125.953.950.6638660.33613413.2928-9.34-261.52-1.548437000
1GermanySHORTSTRAININGregularwomendarkkhakilavender120163125.953.950.6638660.3361342.29281.6646.480.420253000
2GermanyHARDWARE ACCESSORIESGOLFregularwomengoldenrodlavender120163125.953.950.6638660.3361341.70282.2563.000.569620000
3GermanySHOESRUNNINGregularkidsrosybrownlightblue120163125.953.950.6638660.3361349.0028-5.05-141.40-1.278481000
4GermanySHOESRELAX CASUALregularwomenbluevioletlightblue120163125.953.950.6638660.3361349.6028-5.65-158.20-1.430380000
5GermanySWEATSHIRTSTRAININGwidewomensteelbluelightblue120163125.953.950.6638660.3361344.2028-0.25-7.00-0.063291001
6GermanySHOESFOOTBALL GENERICwideunisexbrownrosybrown020163125.953.950.6638660.3361349.9028-5.95-166.60-1.506329000
7GermanySHOESINDOORwidewomenlightskybluerosybrown120163125.953.950.6638660.3361345.2028-1.25-35.00-0.316456001
8GermanyHARDWARE ACCESSORIESRUNNINGslimwomensilverrosybrown120163125.953.950.6638660.3361341.29282.6674.480.673418000
9GermanySHOESFOOTBALL GENERICregularmengrayrosybrown120163125.953.950.6638660.3361348.7028-4.75-133.00-1.202532001
countryproductgroupcategorystylegendermain_colorsec_colorhas_extra_sizesyearmonthweek_numberregular_pricecurrent_priceratiodiscount_pctcostsalesunit_profittotal_profitprofit_marginpromo1promo2label
99945GermanySHOESTRAININGslimwomenchocolatelavender1201662557.9526.950.4650560.53494413.2922713.663100.820.506865000
99946GermanySHORTSTRAININGregularwomendarkkhakilavender1201662557.9526.950.4650560.5349442.2922724.665597.820.915028000
99947GermanyHARDWARE ACCESSORIESGOLFregularwomengoldenrodlavender1201662557.9526.950.4650560.5349441.7022725.255731.750.936920000
99948GermanySHOESRUNNINGregularkidsrosybrownlightblue1201662557.9526.950.4650560.5349449.0022717.954074.650.666048000
99949GermanySHOESRELAX CASUALregularwomenbluevioletlightblue1201662557.9526.950.4650560.5349449.6022717.353938.450.643785000
99950GermanySWEATSHIRTSTRAININGwidewomensteelbluelightblue1201662557.9526.950.4650560.5349444.2022722.755164.250.844156000
99951GermanySHOESFOOTBALL GENERICwideunisexbrownrosybrown0201662557.9526.950.4650560.5349449.9022717.053870.350.632653000
99952GermanySHOESINDOORwidewomenlightskybluerosybrown1201662557.9526.950.4650560.5349445.2022721.754937.250.807050000
99953GermanyHARDWARE ACCESSORIESRUNNINGslimwomensilverrosybrown1201662557.9526.950.4650560.5349441.2922725.665824.820.952134000
99954GermanySHOESFOOTBALL GENERICregularmengrayrosybrown1201662557.9526.950.4650560.5349448.7022718.254142.750.677180000